Created
December 1, 2019 21:48
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update_weights (https://arxiv.org/src/1911.08265v1/anc/pseudocode.py)
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| def update_weights(optimizer: tf.train.Optimizer, network: Network, batch, | |
| weight_decay: float): | |
| loss = 0 | |
| for image, actions, targets in batch: | |
| # Initial step, from the real observation. | |
| value, reward, policy_logits, hidden_state = network.initial_inference( | |
| image) | |
| predictions = [(1.0, value, reward, policy_logits)] | |
| # Recurrent steps, from action and previous hidden state. | |
| for action in actions: | |
| value, reward, policy_logits, hidden_state = network.recurrent_inference( | |
| hidden_state, action) | |
| predictions.append((1.0 / len(actions), value, reward, policy_logits)) | |
| hidden_state = tf.scale_gradient(hidden_state, 0.5) | |
| for prediction, target in zip(predictions, targets): | |
| gradient_scale, value, reward, policy_logits = prediction | |
| target_value, target_reward, target_policy = target | |
| l = ( | |
| scalar_loss(value, target_value) + | |
| scalar_loss(reward, target_reward) + | |
| tf.nn.softmax_cross_entropy_with_logits( | |
| logits=policy_logits, labels=target_policy)) | |
| loss += tf.scale_gradient(l, gradient_scale) | |
| for weights in network.get_weights(): | |
| loss += weight_decay * tf.nn.l2_loss(weights) | |
| optimizer.minimize(loss) |
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